
import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase    :   Tuple						=			logging.get_logger(__name__)

lowercase    :   Dict						=			{
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'sew'







	def __init__(   self						:Any	,  a						:List[str]=3_2	,  a						:Tuple=7_6_8	,  a						:List[str]=1_2	,  a						:int=1_2	,  a						:str=3_0_7_2	,  a						:Optional[int]=2	,  a						:int="gelu"	,  a						:Optional[int]=0.1	,  a						:Any=0.1	,  a						:Optional[Any]=0.1	,  a						:Optional[Any]=0.0	,  a						:Dict=0.1	,  a						:Any=0.1	,  a						:int=0.02	,  a						:str=1E-5	,  a						:Optional[int]="group"	,  a						:int="gelu"	,  a						:Optional[int]=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)	,  a						:Any=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)	,  a						:Union[str, Any]=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)	,  a						:Union[str, Any]=False	,  a						:int=1_2_8	,  a						:Dict=1_6	,  a						:Tuple=True	,  a						:Union[str, Any]=0.05	,  a						:Any=1_0	,  a						:str=2	,  a						:int=0.0	,  a						:Tuple=1_0	,  a						:Optional[int]=0	,  a						:Dict="mean"	,  a						:List[str]=False	,  a						:List[str]=False	,  a						:Dict=2_5_6	,  a						:int=0	,  a						:str=1	,  a						:Any=2	,  **a						:Optional[Any]	,  )						->					List[Any]:
								super().__init__(**a	,  pad_token_id=a	,  bos_token_id=a	,  eos_token_id=a		)
								__UpperCamelCase      :  Dict       =   hidden_size
								__UpperCamelCase      :  List[str]       =   feat_extract_norm
								__UpperCamelCase      :  List[str]       =   feat_extract_activation
								__UpperCamelCase      :  Any       =   list(a		)
								__UpperCamelCase      :  Optional[int]       =   list(a		)
								__UpperCamelCase      :  str       =   list(a		)
								__UpperCamelCase      :  Optional[int]       =   conv_bias
								__UpperCamelCase      :  Dict       =   num_conv_pos_embeddings
								__UpperCamelCase      :  str       =   num_conv_pos_embedding_groups
								__UpperCamelCase      :  Optional[int]       =   len(self.conv_dim		)
								__UpperCamelCase      :  List[str]       =   num_hidden_layers
								__UpperCamelCase      :  List[Any]       =   intermediate_size
								__UpperCamelCase      :  int       =   squeeze_factor
								__UpperCamelCase      :  List[str]       =   hidden_act
								__UpperCamelCase      :  int       =   num_attention_heads
								__UpperCamelCase      :  Any       =   hidden_dropout
								__UpperCamelCase      :  List[Any]       =   attention_dropout
								__UpperCamelCase      :  Union[str, Any]       =   activation_dropout
								__UpperCamelCase      :  Dict       =   feat_proj_dropout
								__UpperCamelCase      :  Tuple       =   final_dropout
								__UpperCamelCase      :  Union[str, Any]       =   layerdrop
								__UpperCamelCase      :  Tuple       =   layer_norm_eps
								__UpperCamelCase      :  Optional[Any]       =   initializer_range
								__UpperCamelCase      :  Optional[Any]       =   vocab_size

								if (
								    (len(self.conv_stride		) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
								    or (len(self.conv_dim		) != self.num_feat_extract_layers)
								):
															raise ValueError(
															    "Configuration for convolutional layers is incorrect."
															    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
															    f'but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)'
															    f'= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'		)

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								__UpperCamelCase      :  Dict       =   apply_spec_augment
								__UpperCamelCase      :  Union[str, Any]       =   mask_time_prob
								__UpperCamelCase      :  Any       =   mask_time_length
								__UpperCamelCase      :  List[str]       =   mask_time_min_masks
								__UpperCamelCase      :  int       =   mask_feature_prob
								__UpperCamelCase      :  Optional[Any]       =   mask_feature_length
								__UpperCamelCase      :  Any       =   mask_feature_min_masks

								# ctc loss
								__UpperCamelCase      :  Dict       =   ctc_loss_reduction
								__UpperCamelCase      :  List[str]       =   ctc_zero_infinity

								# sequence classification
								__UpperCamelCase      :  Optional[Any]       =   use_weighted_layer_sum
								__UpperCamelCase      :  str       =   classifier_proj_size







	@property
	def   _lowerCamelCase							(   self						:str		)						->					Tuple:
								return functools.reduce(operator.mul	,  self.conv_stride	,  1		)

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowercase    :   List[Any]						=			'Run commands across TPU VMs for initial setup before running `accelerate launch`.'




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any]=None)   ->       List[str]:






							'''simple docstring'''





							if subparsers is not None:
														__UpperCamelCase      :  int       =   subparsers.add_parser("tpu-config"		,    description=_description)
							else:
														__UpperCamelCase      :  Dict       =   argparse.ArgumentParser("Accelerate tpu-config command"		,    description=_description)
							# Core arguments
							__UpperCamelCase      :  Optional[Any]       =   parser.add_argument_group(
							    "Config Arguments"		,    "Arguments that can be configured through `accelerate config`.")
							config_args.add_argument(
							    "--config_file"		,    type=_lowerCamelCase		,    default=_lowerCamelCase		,    help="Path to the config file to use for accelerate."		,    )
							config_args.add_argument(
							    "--tpu_name"		,    default=_lowerCamelCase		,    help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."		,    )
							config_args.add_argument(
							    "--tpu_zone"		,    default=_lowerCamelCase		,    help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."		,    )
							__UpperCamelCase      :  Tuple       =   parser.add_argument_group("TPU Arguments"		,    "Arguments for options ran inside the TPU.")
							pod_args.add_argument(
							    "--use_alpha"		,    action="store_true"		,    help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."		,    )
							pod_args.add_argument(
							    "--command_file"		,    default=_lowerCamelCase		,    help="The path to the file containing the commands to run on the pod on startup."		,    )
							pod_args.add_argument(
							    "--command"		,    action="append"		,    nargs="+"		,    help="A command to run on the pod. Can be passed multiple times."		,    )
							pod_args.add_argument(
							    "--install_accelerate"		,    action="store_true"		,    help="Whether to install accelerate on the pod. Defaults to False."		,    )
							pod_args.add_argument(
							    "--accelerate_version"		,    default="latest"		,    help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."		,    )
							pod_args.add_argument(
							    "--debug"		,    action="store_true"		,    help="If set, will print the command that would be run instead of running it.")

							if subparsers is not None:
														parser.set_defaults(func=_lowerCamelCase)
							return parser




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(_lowerCamelCase):
														__UpperCamelCase      :  Dict       =   load_config_from_file(args.config_file)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					__UpperCamelCase      :  List[Any]       =   defaults.command_file
														if not args.command and defaults.commands is not None:
																					__UpperCamelCase      :  int       =   defaults.commands
														if not args.tpu_name:
																					__UpperCamelCase      :  int       =   defaults.tpu_name
														if not args.tpu_zone:
																					__UpperCamelCase      :  Optional[Any]       =   defaults.tpu_zone
							if args.accelerate_version == "dev":
														__UpperCamelCase      :  List[str]       =   "git+https://github.com/huggingface/accelerate.git"
							elif args.accelerate_version == "latest":
														__UpperCamelCase      :  Optional[int]       =   "accelerate -U"
							elif isinstance(parse(args.accelerate_version)		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   F'accelerate=={args.accelerate_version}'

							if not args.command_file and not args.command:
														raise ValueError("You must specify either a command file or a command to run on the pod.")

							if args.command_file:
														with open(args.command_file		,    "r") as f:
																					__UpperCamelCase      :  List[Any]       =   [f.read().splitlines()]

    # To turn list of lists into list of strings
							if isinstance(args.command[0]		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   [line for cmd in args.command for line in cmd]
							# Default to the shared folder and install accelerate
							__UpperCamelCase      :  str       =   ["cd /usr/share"]
							if args.install_accelerate:
														new_cmd += [F'pip install {args.accelerate_version}']
							new_cmd += args.command
							__UpperCamelCase      :  str       =   "; ".join(_lowerCamelCase)

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							__UpperCamelCase      :  List[Any]       =   ["gcloud"]
							if args.use_alpha:
														cmd += ["alpha"]
							cmd += [
							    "compute",
							    "tpus",
							    "tpu-vm",
							    "ssh",
							    args.tpu_name,
							    "--zone",
							    args.tpu_zone,
							    "--command",
							    args.command,
							    "--worker",
							    "all",
							]
							if args.debug:
														print(F'Running {" ".join(_lowerCamelCase)}')
														return
							subprocess.run(_lowerCamelCase)
							print("Successfully setup pod.")




def       _SCREAMING_SNAKE_CASE   (						)   ->       List[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Optional[Any]       =   tpu_command_parser()
							__UpperCamelCase      :  Optional[Any]       =   parser.parse_args()

							tpu_command_launcher(_lowerCamelCase)